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<p>With the advent of the information age, data has become an important resource and production factor. However, the existence of abnormal data causes the lose of personal privacy, business operations and national security, therefore, anomaly detection has received increasing attention in recent years. Most existing anomaly detection models are based on machine learning or deep learning models, but the use of a single model leads to the problems such as overfitting, weak generalization and poor stability. Meanwhile, there is a serious data imbalance problem due to the significantly few number of abnormal data compared to normal data, which reduces the detection performance. To address these issues, this paper proposes an anomaly detection model called VAHMSE based on <i>v</i>ariational <i>a</i>utoencoder and <i>h</i>eterogeneous <i>m</i>ulti-<i>s</i>tacking <i>e</i>nsemble learning to improve the detection performance. In the data augmentation phase, the <i>v</i>ariational <i>a</i>uto<i>e</i>ncoder (VAE) is used to replace traditional oversampling and other class balancing techniques to solve the data imbalance problem, and the mutual information is added to the loss function of traditional VAE to solve the problem of posterior distribution collapsing to prior distribution, thereby improving the quality of data generation. In the anomaly detection phase, the heterogeneous multi-stacking ensemble learning-based anomaly detection method is proposed, where five machine learning models with good performance are selected as the base learners in the first layer stacking process, and the TCN is selected as the meta learner in the second layer stacking process; In addition, the Squeeze and Excitation module is integrated into the traditional TCN model to explicitly model the interdependence between convolutional feature channels and improve the representation ability of network. Extensive experiments on six widely used datasets show that compared with five state-of-the-art models, the proposed VAHMSE achieves better performance in accuracy, recall, precision and F1-score, and it also achieves better stability.</p>

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VAHMSE: an efficient anomaly detection model based on variational autoencoder and heterogeneous multi-stacking ensemble learning

  • 期刊论文

  • 发表日期：

    2025年8月30日

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  • Rui Wang,
  • Jiayao Li

摘要

With the advent of the information age, data has become an important resource and production factor. However, the existence of abnormal data causes the lose of personal privacy, business operations and national security, therefore, anomaly detection has received increasing attention in recent years. Most existing anomaly detection models are based on machine learning or deep learning models, but the use of a single model leads to the problems such as overfitting, weak generalization and poor stability. Meanwhile, there is a serious data imbalance problem due to the significantly few number of abnormal data compared to normal data, which reduces the detection performance. To address these issues, this paper proposes an anomaly detection model called VAHMSE based on variational autoencoder and heterogeneous multi-stacking ensemble learning to improve the detection performance. In the data augmentation phase, the variational autoencoder (VAE) is used to replace traditional oversampling and other class balancing techniques to solve the data imbalance problem, and the mutual information is added to the loss function of traditional VAE to solve the problem of posterior distribution collapsing to prior distribution, thereby improving the quality of data generation. In the anomaly detection phase, the heterogeneous multi-stacking ensemble learning-based anomaly detection method is proposed, where five machine learning models with good performance are selected as the base learners in the first layer stacking process, and the TCN is selected as the meta learner in the second layer stacking process; In addition, the Squeeze and Excitation module is integrated into the traditional TCN model to explicitly model the interdependence between convolutional feature channels and improve the representation ability of network. Extensive experiments on six widely used datasets show that compared with five state-of-the-art models, the proposed VAHMSE achieves better performance in accuracy, recall, precision and F1-score, and it also achieves better stability.